Claire Little

Claire Little is a Researcher at the Cathie Marsh Institute for Social Research. Her work focuses on synthetic data generation, federated learning, and privacy-preserving data techniques. She is actively involved in the Centre for Digital Trust and Society, exploring interdisciplinary approaches to data trust and societal impact.

Her research interests include synthetic data synthesis for census microdata, federated learning applications, and the trade-offs between data utility and disclosure risk. She has contributed to methodologies for creating synthetic teaching datasets without original data access, enhancing educational and research environments.

Her publications emphasize innovative techniques like multi-objective evolutionary GANs for tabular data synthesis and comparative studies of synthesis methods. Claire’s work bridges computer science and social statistics, addressing both technical and ethical challenges in data utilization.

Collaborations include global research networks exploring synthetic data validation and federated learning frameworks. She has been recognized for her contributions to privacy-preserving data science through peer-reviewed publications and conference presentations.

Key affiliations include the Cathie Marsh Institute and the Christabel Pankhurst Institute, where she engages with digital futures and social research initiatives.